SAN FRANCISCO — Have you ever heard the phrase "Let sleeping dogs lie" in casual conversation? Does "If it ain't broke, don't fix it" ring any bells? Well, those all-too-familiar cliches can often be applied to customers. Certain customers, for example, are utterly content in the present. If a customer matching that description is sent what's known as a "trigger offer," it can disrupt their prior contentment -- which might inadvertently promote churn. But how do you know which customers not to trigger? How do you segment your customer base and cut costs at the same time? This scenario was brought to light here at the Hotel Nikko in San Francisco this morning, as part of the first-ever Predictive Analytics World conference, when Dr. Eric Siegel, president of Prediction Impact and the event's program chair, delivered an opening keynote that outlined some best practices for predictive analytics, and identified the value each can provide.

"Your organization learns from its collective experience," Siegel said, explaining how models for predictive analytics rest atop existing customer data, gaining insight from information your company already has. Because of its base in operational data, Siegel told the crowd, predictive analytics -- a business intelligence technology that produces a predictive score for each customer or prospect -- has the most potential to positively influence operational decisions. And despite acknowledging a hatred of bullet-point lists, Siegel shared with attendees a list of nine applications for predictive analytics -- in precisely that format:

response modeling

customer retention with churn modeling

product recommendations

online-marketing optimization

behavior-based advertising

email targeting

insurance pricing

credit scoring

lead scoring

And the list keeps growing, Siegel said -- as does the list of interested verticals, which has expanded from the set of early adopters (financial services, e-commerce, healthcare) to include consumer services, high technology, insurance, nonprofits, publishing, retail, and telecommunications. Although each industry has unique needs, Siegel said, predictive modeling crosses industry lines -- and brings potential cost-saving benefits along with it.

Siegel's top-five list for lowering costs with predictive analytics:

1. Don't contact those who won't respond.Response modeling -- This type of modeling basically means targeting fewer people without sacrificing a high response rate. By modeling on top of historical customer data, you can see who is likely to respond to an offer, and then target accordingly.

2. Don't contact those who would have made a purchase anyway. Response-uplift modeling -- The next step after response modeling involves segmenting out those who would likely purchase regardless of an offer. Having identified this segment, you can save money by removing them from the campaign.

3. Don't waste expensive retention offers on those who will stay anyway. Churn modeling -- "Retaining customers can be quite expensive," Siegel said. "You can't afford to offer [retention incentives] to all of your customers. If you predict those most likely to leave, you can target much more efficiently."

4. Don't offend those who would otherwise stay. Churn-uplift modeling -- That cliche is true: It's sometimes best to let sleeping dogs lie. Some customers would never have taken their money elsewhere until you reminded them you had it. It's better just to leave these stagnant customers alone. You don't want to remind them that they're paying you, or annoy them with unnecessary offers. In order to do this, you have to model a control set of customers to properly segment -- and to measure the value of modeling initiatives.

5. Don't acquire "loss customers." Risk modeling -- You wouldn't lend your credit card to a convicted thief, would you? By the same token, you don't want to sell to customers who lack the means or the intention to pay. Risk modeling is especially crucial for credit-card providers and insurance companies, where if things go wrong the provider eats the cost.

Siegel added that there are still other cost-saving measures that can be achieved through predictive analytics: fraud detection, application processing, and supply-chain optimization, to name just a few.

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